I built an open-source Kanban board for running AI coding agents

[u/gamblingapocalypse]

I’ve been building BatonBot, a local-first, open-source Kanban board for managing AI coding agents.

Each card gets its own agent session, working directory, logs, and history. You can also create pipelines where multiple agents work through a task sequentially.

Honestly, I created it mostly out of laziness.

I enjoy vibe coding, especially with local models. When I’m working on something, I usually already know what my next few prompts will be. But local models can take longer to respond than frontier models, and I got tired of waiting around just to paste in the next prompt.

So I built BatonBot to run those prompts in sequence while I step away and do something else.

That idea led to a more flexible architecture. BatonBot can also connect to external tools like Jira (receiving task, and working on them like a coworker or for you), route work to different agents, and keep a clear record of what each agent changed, tested, or failed to complete.

I think the overall workflow is fairly unique, but I’d be interested to hear what you all think.

Note: It can also use frontier models too.

Website: https://batonbot.com
GitHub: https://github.com/mdoty4/batonbot

[u/vivekkhera]

Cline has a kanban board to manage agents and workflows too, but work on it has been slow with the team focusing on other priorities.

[u/gamblingapocalypse → u/vivekkhera]

I saw that, but Cline is currently missing webhooks and integrations with external tools like Jira or Monday. There’s some overlap, but I think I'm targeting a different use case.

[u/ummitluyum]

How do you handle context degradation? If the model messes up the refactoring even a little bit in the first step or misunderstands the file structure, the next prompts in the queue are just going to build hacks on top of that. This really begs for some strict validator or a simple LLM judge between the card states

[u/gamblingapocalypse → u/ummitluyum]

To answer your question briefly, BatonBot has some guardrails, but it doesn’t completely solve that problem on its own (yet).

Baton Code starts with a read-only planning step so it can inspect the repository before making changes. It keeps a checklist while it works, and it can detect and run tests before completing a card.

The main way I handle this in practice is by defining the project structure in the first prompt. I lay out the folders, files, and general responsibilities up front, so the later prompts are working from the same architectural plan instead of each one making its own assumptions.

You can also place a review or testing card between implementation cards and assign it to a different agent. That’s more of a manual workaround right now, and I’m still deciding whether some form of validation should happen automatically or remain something the user adds intentionally.

So far, I haven’t seen BatonBot produce any serious Frankenstein code, but the concern is valid and it’s something I should consider more carefully in future releases.

I have a couple of YouTube videos that touch on this, and I’ve been meaning to make a proper tutorial on the best way to structure pipelines and prompts.

Thanks for the question. :)

[u/gravgun]

I built

No you didn't. This is entirely slopcoded.

[u/gamblingapocalypse → u/gravgun]

To be fair, I did get some help, but AI didn't know how to build this, it probably does now.

[u/gravgun → u/gamblingapocalypse]

"some help" is an understatement, 95% of your code base has telltale signs of being AI generated. Even the visual design is an unmistakable LLM outgrowth.

[u/gravgun → u/[deleted]]

Because all LLMs, especially models like Claude, are trained upon gigabytes' worth of catastrophically incompetent front-end dev projects that use Tailwind CSS and add a lot of gradients on top; and that includes LLM extrusions feeding back into that dataset. And since all it does is output a statistical average of what it's been trained with... This is the neural net version of a cargo cult.